Text analysis: title and abstract of male and female speakers

Abstracts

data <- read.table("data/presentations_PPGE_2008-2019.csv", sep=",",
                   header=T, as.is=T)
data$date <- dmy(data$date)
data$year <- year(data$date) 
#skimr::skim(data)

Excluding special events as round tables and discussions not related to a project or study presented by someone.

IDs <- c(154, 250, 211, 289)
data <- data %>% filter(!id %in% IDs)

Using abstracts in English (original or translated)

data <- data  %>% filter(!is.na(abstract_english))

Number of abstracts per group

table(data$gender)

## 
##   F   M 
## 101 139

table(data$position_cat,data$gender)

##            
##              F  M
##   others     4  1
##   postdoc   21 21
##   professor 21 60
##   student   53 56

Tidytext

text_tok <- data %>% dplyr::select(id,gender,position_cat, audience_n,
                             abstract_english, title_english) %>%
  mutate(text = paste(title_english, abstract_english)) %>%
  unnest_tokens(output=word,input=text)

stop_w <- tibble(word = stopwords("en"))

# remove stopwords
text <- text_tok %>% 
  anti_join(stop_w, by="word")  %>% arrange(word) 

# remove numbers and other characters
text <- text %>% slice(-c(1:290)) %>% # number and some symbols
          filter(nchar(word)!=1) %>% # letters alone
          filter(!word %in% c("mpas", "δ13c", "β") )# remove acronyms, symbols


# solving some simple plurals
plural <- c("actions","advances", "adaptations", "amphibians", "animals", "ants","anurans",
            "applications","approaches", "bees","builds", "birds",
            "cerrados","challenges",
            "continents","crops", 
            "decisions","declines","determines","determinants", "defenses",
            "dynamics",
            "economics", "ecosystems","environments", "experiences",
            "forests",
            "genetics","gifts","gradients","guides","impacts",
            "increases","interactions","lives",
            "landscapes","males","mammals", "mangroves","models","movements",
            "mutualisms","networks","neotropics",
            "opilions","phenotypes","plants","projects","paths", "perspectives",
            "populations","promotes","relationships", "relations",
            "resources","responses","roads","services","skulls","snakes","seeds",
            "spaces", "spiders","stages", "trees", "variations",
            "threats")

text$word[text$word %in% plural] <- 
  substr(text$word[text$word %in% plural],
       1,nchar(text$word[text$word %in% plural])-1)

Grouping similar words:

lemma <- rbind(c("adaptive", "adaptation"),
               c("advancement", "advance"),
               c("agricultural", "agriculture"),
               c("agro", "agriculture" ),
               c("amazonia","amazon" ),
               c("amazonian","amazon" ),
               c("andean","andes"),
               c("apply","application"),
               c("applying","application"),
               c("apidae","apis"),
               c("arachnida","arachnid"),
               c("argue","argument"),
               c("basal", "basis"),
               c("behavioral","behavior"),
               c("behavioural","behavior"),
               c("bignonieae", "bignoniaceae"),
               c("biological", "biology"),
               c("brazilian","brazil"),
               c("building","build"),
               c("changing", "change"),
               c("cnidarian", "cnidaria"),
               c("coastal","coast"),
               c("colour", "color"),
               c("colors", "color"),
               c("communities","community" ),
               c("competitive", "competition"),
               c("complexity", "complex"),
               c("convergences", "convergence"),
               c("convergent", "convergence"),
               c("cordatus","cordata" ),
               c("croplands","crop"),
               c( "cultural", "culture"),
               c("darwin's", "darwin"),
               c("darwinian", "darwin"),
               c("defensive", "defense"),
               c("dependent","dependence"),
               c("detecting","detection"),
               c("determine", "determinant"),
               c("developmental", "development"),
               c("dispersers","dispersal"),
               c("disturbed", "disturbance"),
               c("diversification", "diversity"),
               c("dragonflies", "dragonfly"),
               c("drier", "drought"),
               c("ecological", "ecology"),
               c("ecologists", "ecology"),
               c("endemic", "endemism"),
               c("effectiveness", "efficiency"),
               c("environmental", "environment"),
               c("evolutionary", "evolution"),
               c("expanding", "expansion"),
               c("extinct", "extinction"),
               c("facilitate", "facilitation"),
               c("fisheries", "fishery"),
               c("floral", "flora"),
               c("floristic", "flora"),
               c("forested", "forest"),
               c("functional", "function"),
               c("functionally", "function"),
               c("functioning", "function"),
               c("geographical", "geographic"),
               c("heterogeneties", "heterogeneity"),
               c("heterogeneous", "heterogeneity"),
               c("histories", "history"),
               c("integrated", "integration"),
               c("intregating", "integration"),
               c("integrative", "integration"),
               c("invasive", "invasion"),
               c("isotopic", "isotope"),
               c("linking", "link"),
               c("living", "live"),
               c("mammalia", "mammal"),
               c("managed", "manage"),
               c("managers", "manage"),
               c("mathematical", "mathematics"),
               c("mates", "mating"),
               c("mediated", "mediate"),
               c("mechanistic", "mechanism"),
               c("matrices", "matrix"),
               c("migratory", "migration"),
               c("mimicking", "mimicry"),
               c("modeling", "model"),
               c("mutualistic", "mutualism"),
               c("natural", "nature"),
               c("neotropical", "neotropic"),
               c("northeastern", "northeast"),
               c("occuring", "occur"),
               c("onça", "onca"),
               c("opiliones", "opilion"),
               c("parasite", "parasitism"),
               c("parent", "parenting"),
               c("phylogenies", "phylogeny"),
               c("phylogenetic", "phylogeny"),
               c("phylogenomic", "phylogeny"),
               c("pollinators", "pollination"),
               c("protected", "protect"),
               c("protective", "protect"),
               c("rainfall", "rain"),
               c("reconstructing", "reconstruction"),
               c("regulatory", "regulation"),
               c("regulates", "regulation"),
               c("relation", "relationship"),
               c("reproductive", "reproduction"),
               c("restored", "restoration"),
               c("robustness", "robust"),
               c("scientific", "science"),
               c("scientist", "science"),
               c("sexy", "sexual"),
               c("simulated", "simulation"),
               c("societies", "society"),
               c("social", "society"),
               c("socio", "society"),
               c("space", "spatial"),
               c("spacio", "spatial"),
               c("stabilize", "stability"),
               c("stable", "stability"),
               c("stories", "story"),
               c("strategic", "strategy"),
               c("strategies", "strategy"),
               c("structured", "structure"),
               c("structuring", "structure"),
               c("studies", "study"),
               c("studing", "study"),
               c("sustainable", "sustainability"),
               c("theories", "theory"),
               c("theoretical", "theory"),
               c("threatened", "threat"),
               c("tropical", "tropic"),
               c("vision", "visual")
               )
lemma <- as.data.frame(lemma)

for (i in 1:dim(lemma)[1]){
  text$word[text$word == lemma[i,1]] <- lemma[i,2]
}

WORDS - aLl data

table(text$gender)

## 
##     F     M 
## 10812 13614

table(text$position_cat ,text$gender)

##            
##                F    M
##   others     262  139
##   postdoc   2793 2494
##   professor 2062 5370
##   student   5524 5531

Mean number of words by abstract

text %>% count(id,gender) %>%
  ggplot(aes(x=gender, y=n)) + 
  geom_violin() + geom_boxplot(width=0.2)+
  ggbeeswarm::geom_quasirandom(size=3, shape=21)

20 palavra mais comuns

text %>%
  count(word, sort = TRUE) %>% 
  top_n(20,n)%>%
  kable()

word	n
species	384
ecology	188
forest	175
model	157
study	156
environment	137
evolution	134
can	132
landscape	127
population	124
diversity	112
nature	102
community	100
male	97
plant	97
different	95
patterns	88
present	88
areas	84
interaction	82

Word cloud

textplot_wordcloud(x=dfm(tokens(text$word)))

par(mfrow=c(1,2))
textplot_wordcloud(x=dfm(tokens(text$word[text$gender=="F"])),
                   col="#6D57CF")
par(new=T)
textplot_wordcloud(x=dfm(tokens(text$word[text$gender=="M"])),
                   col="#FCA532")

Word frequencies by gender

props <- text %>%
  count(gender, word) %>%
  group_by(gender) %>%
  mutate(proportion = n / sum(n)) %>% 
  pivot_wider(names_from = gender, values_from = c(proportion,n)) %>%
  mutate(abs.dif.p = abs(proportion_F-proportion_M),
         rel.dif.p = pmax(proportion_F, proportion_M)/
           pmin(proportion_F, proportion_M)) %>%
  arrange(desc(abs.dif.p))
props$label <- NA
props$label[1:20] <- props$word[1:20]

ggplot(props, aes(x=proportion_M,, y=proportion_F,
       color=abs.dif.p)) + 
  geom_abline(color = "gray40", lty = 2) +
  #geom_point(size=2.5, alpha=0.5)+
  geom_jitter(size=2.5, alpha=0.2)+
  geom_text_repel(aes(label=label), size=3.2)+
  scale_x_log10(name="Male most used words",
                labels = percent_format()) +
  scale_y_log10(name="Female most used words",
                labels = percent_format()) +
  scale_color_gradient(name="Abs Diff",low = "blue", high = "red",
                       labels=percent_format())  +
  theme(legend.justification = c(1, -0.1), legend.position = c(1, 0))

 # geom_smooth(method="lm")
ggsave("figures/abstract_wordFreq.jpg", height = 5, width=7)

Words that are close to the dashed line in these plots have similar frequencies in both genders. Words that are far from the line are words that are found more in one set of texts than another.

Legend: absolute differences in the frequency of the word by males and females. Differences above 0.3% are also indicated in text.

Correlation of word frequeency use between gender:

cor.test(props$proportion_F, props$proportion_M)

## 
##  Pearson's product-moment correlation
## 
## data:  props$proportion_F and props$proportion_M
## t = 70.789, df = 1677, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8530819 0.8771137
## sample estimates:
##       cor 
## 0.8655954

Highly correlated -> it means they tend to use the same frequency of main word

20 words with the largest differences in frequency

prop2 <- props %>% filter(!is.na(label)) %>%
  arrange(desc(proportion_F), desc(proportion_M)) %>%
  mutate(ntot = n_F + n_M) %>%
  mutate(word = fct_reorder(word,(ntot),max),
         proportion_F = proportion_F*-1) %>%
  pivot_longer(2:3,names_to = "gender", values_to ="proportion")

ggplot(prop2, aes(x=proportion, y=word,fill=gender)) +
  geom_col()+ ylab("") + xlab("Proportion")+
  scale_fill_manual(name="gender", values=c("#6D57CF","#FCA532"),
                    labels=c("F", "M"))+
   geom_vline(xintercept = c(-0.02,-0.01,0,0.01,0.02),
              linetype="dotted",
             col="darkgray") +
  scale_x_continuous(breaks=c(-0.02,-0.01,0,0.01,0.02),
                     labels = c(0.02,0.01,0,0.01,0.02))

ggsave("figures/abstract_wordFreq_barplot.jpeg", units="in", width=7, height=7, dpi=300)

TF IDF

text_id <- text %>% count(gender, word) %>% 
  bind_tf_idf(word, gender, n) %>%
  arrange(desc(tf_idf))

10 “exclusive” words for each group

text_id$word <- as.factor(text_id$word)
text_id %>%
  group_by(gender) %>% 
  arrange(desc(tf_idf)) %>% 
  top_n(10, tf_idf) %>%  
  ggplot(aes(x = tf_idf, y = reorder(word, tf_idf), fill = gender)) +
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = "tf-idf") +
  facet_wrap(~gender, scales = "free") +
  theme_minimal()

WORDS professors only data

textP <- text %>% filter(position_cat == "professor")

table(textP$gender)

## 
##    F    M 
## 2062 5370

Mean number of words by abstract

textP %>% count(id,gender) %>%
  ggplot(aes(x=gender, y=n)) + 
  geom_violin() + geom_boxplot(width=0.2)+
  ggbeeswarm::geom_quasirandom(size=3, shape=21)

20 palavra mais comuns

textP %>%
  count(word, sort = TRUE) %>% 
  top_n(20,n)%>%
  kable()

word	n
species	88
ecology	67
evolution	52
population	52
environment	50
nature	44
plant	43
model	41
study	41
can	39
ecosystem	38
diversity	35
society	32
water	32
pollination	30
research	30
interaction	29
science	29
biology	28
present	26

Counting words Frequency by gender

propsP <- textP %>%
    count(gender, word) %>%
  group_by(gender) %>%
  mutate(proportion = n / sum(n)) %>% 
  pivot_wider(names_from = gender, values_from = c(proportion,n)) %>%
  mutate(abs.dif.p = abs(proportion_F-proportion_M),
         rel.dif.p = pmax(proportion_F, proportion_M)/
           pmin(proportion_F, proportion_M)) %>%
  arrange(desc(abs.dif.p))
propsP$label <- NA
propsP$label[1:20] <- propsP$word[1:20]

ggplot(propsP, aes(x=proportion_M, y=proportion_F,
       color=abs.dif.p)) + 
  geom_abline(color = "gray40", lty = 2) +
 # geom_point(size=2.5, alpha=0.3) +
  geom_jitter(size=2.5, alpha=0.3)+
  geom_text_repel(aes(label=label), size=3)+
  scale_x_log10(name="Male most used words",   limits=c(0.0003,0.02),
                labels = percent_format()) +
  scale_y_log10(name="Female Most used words", limits=c(0.0003,0.02),
                labels = percent_format()) +
  scale_color_gradient(name="Abs Diff",low = "blue", high = "red",
                       labels=percent_format())  +
  theme(legend.justification = c(1, -0.1), legend.position = c(1, 0))

 # geom_smooth(method="lm")
ggsave("figures/abstract_wordFreq_Prof.jpg", height = 5, width=7)

Words that are close to the dashed line in these plots have similar frequencies in both genders. Words that are far from the line are words that are found more in one set of texts than another.

Legend: absolute differences in the frequency of the word by males and females.

Labels for the 20 words with largest differences in frequency.

Correlation of word frequeency use between gender:

cor.test(propsP$proportion_F, propsP$proportion_M)

## 
##  Pearson's product-moment correlation
## 
## data:  propsP$proportion_F and propsP$proportion_M
## t = 20.607, df = 559, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6072948 0.7016627
## sample estimates:
##       cor 
## 0.6570452

20 words with the largest differences in frequency

propP2 <- propsP %>% filter(!is.na(label)) %>%
  arrange(desc(proportion_F), desc(proportion_M)) %>%
  mutate(ntot = n_F + n_M) %>%
  mutate(word = fct_reorder(word,(ntot),max),
         proportion_F = proportion_F*-1) %>%
  pivot_longer(2:3,names_to = "gender", values_to ="proportion")

ggplot(propP2, aes(x=proportion, y=word,fill=gender)) +
  geom_col()+ ylab("") + xlab("Proportion")+
  scale_fill_manual(name="gender", values=c("#6D57CF","#FCA532"),
                    labels=c("F", "M"))+
   geom_vline(xintercept = c(-0.03,-0.02,-0.01,0,0.01,0.02,0.03),
              linetype="dotted",
             col="darkgray") +
  scale_x_continuous(breaks=c(-0.03,-0.02,-0.01,0,0.01,0.02,0.03),
                     labels = c(0.03,0.02,-0.01,0,0.01,0.02,0.03))

ggsave("figures/abstract_wordFreq_barplot_Prof.jpeg", units="in", width=7, height=7, dpi=300)

Topic model - all data

matext <- text %>% count(id, gender, word) %>% mutate(id = paste(id, gender, sep="_")) %>%
  select(-gender) %>%
                cast_dtm(term=word,document=id,value=n)

3 models with different number of topics, comparing AIC - cross validation is better? loo

ap_lda2 <- LDA(matext, k = 2, control = list(seed = 1234))
ap_lda3 <- LDA(matext, k = 3, control = list(seed = 1234))
ap_lda4 <- LDA(matext, k = 4, control = list(seed = 1234))
bbmle::AICtab(ap_lda2, ap_lda3, ap_lda4,base=T)

##         AIC      dAIC     df   
## ap_lda2 377783.9      0.0 9919 
## ap_lda3 379803.9   2020.0 14878
## ap_lda4 383258.3   5474.4 19837

Word-topic probabilities

10 words with the largest probabilities for each group

ap_topics <- tidy(ap_lda2, matrix = "beta")
ap_top_terms <- ap_topics %>%
      group_by(topic) %>%
      top_n(10, beta) %>%
      ungroup() %>%
      arrange(topic, -beta)
ap_top_terms %>%
mutate(term = reorder(term, beta)) %>% ggplot(aes(term, beta, fill = factor(topic))) + geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") + coord_flip()

Document-topic probabilities - classifying the abstracts

ap_documents <- tidy(ap_lda2, matrix = "gamma")
classifi <- ap_documents %>% mutate(gender = substr(document, nchar(document), nchar(document))) %>%
  group_by(document,gender) %>%
      top_n(1, gamma) 

table(classifi$gender, classifi$topic)

##    
##      1  2
##   F 55 46
##   M 62 76

classifi %>% tabyl(gender, topic) %>% adorn_percentages() %>% 
  adorn_pct_formatting(digits = 0)

##  gender   1   2
##       F 54% 46%
##       M 45% 55%

classifi %>%
 # mutate(title = reorder(title, gamma * topic)) %>%
  ggplot(aes(as.character(topic), gamma)) +
  geom_boxplot() +
  facet_wrap(~ gender)

Topic model Professors only

matextP <- textP %>% 
  count(id, gender, word) %>% mutate(id = paste(id, gender, sep="_")) %>%
  select(-gender) %>%
                cast_dtm(term=word,document=id,value=n)

ap_lda2 <- LDA(matextP, k = 2, control = list(seed = 1234))
ap_lda3 <- LDA(matextP, k = 3, control = list(seed = 1234))
ap_lda4 <- LDA(matextP, k = 4, control = list(seed = 1234))
bbmle::AICtab(ap_lda2, ap_lda3, ap_lda4,base=T)

##         AIC      dAIC     df   
## ap_lda2 113448.6      0.0 5203 
## ap_lda3 115229.2   1780.6 7804 
## ap_lda4 117829.0   4380.4 10405

word-topic probabilities

ap_topics <- tidy(ap_lda2, matrix = "beta")
ap_top_terms <- ap_topics %>%
      group_by(topic) %>%
      top_n(10, beta) %>%
      ungroup() %>%
      arrange(topic, -beta)
ap_top_terms %>%
mutate(term = reorder(term, beta)) %>% ggplot(aes(term, beta, fill = factor(topic))) + geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") + coord_flip()

Document-topic probabilities

ap_documents <- tidy(ap_lda2, matrix = "gamma")
classifi <- ap_documents %>% mutate(gender = substr(document, nchar(document), nchar(document))) %>%
  group_by(document,gender) %>%
      top_n(1, gamma) 

table(classifi$gender, classifi$topic)

##    
##      1  2
##   F 11 10
##   M 36 23

library(janitor)
classifi %>% tabyl(gender, topic) %>% adorn_percentages() %>% 
  adorn_pct_formatting(digits = 0)

##  gender   1   2
##       F 52% 48%
##       M 61% 39%

classifi %>%
 # mutate(title = reorder(title, gamma * topic)) %>%
  ggplot(aes(as.character(topic), gamma)) +
  geom_boxplot() +
  geom_violin()+
  facet_wrap(~ gender)

Sentiment analysis

Chapter 2, Silge & RObinson. 2018

The NRC lexicon categorizes words in a binary fashion (“yes”/“no”) into categories of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.

get_sentiments("nrc")

## # A tibble: 13,875 × 2
##    word        sentiment
##    <chr>       <chr>    
##  1 abacus      trust    
##  2 abandon     fear     
##  3 abandon     negative 
##  4 abandon     sadness  
##  5 abandoned   anger    
##  6 abandoned   fear     
##  7 abandoned   negative 
##  8 abandoned   sadness  
##  9 abandonment anger    
## 10 abandonment fear     
## # … with 13,865 more rows

The Bing lexicon categorizes words in a binary fashion into positive and negative categories.

get_sentiments("bing")

## # A tibble: 6,786 × 2
##    word        sentiment
##    <chr>       <chr>    
##  1 2-faces     negative 
##  2 abnormal    negative 
##  3 abolish     negative 
##  4 abominable  negative 
##  5 abominably  negative 
##  6 abominate   negative 
##  7 abomination negative 
##  8 abort       negative 
##  9 aborted     negative 
## 10 aborts      negative 
## # … with 6,776 more rows

The AFINN lexicon assigns words with a score that runs between -5 and 5, with neg‐ ative scores indicating negative sentiment and positive scores indicating positive sen‐ timent.

get_sentiments("afinn")

## # A tibble: 2,477 × 2
##    word       value
##    <chr>      <dbl>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # … with 2,467 more rows

PENSAR: tem que levar em conta número de palavras diferentes entre abstracts - principalmente se ouver diferença média de número de palavras por abstract de homens e mulehres né? ou não?

Score words difference in female and male abstracts

All data

affword <- get_sentiments("afinn")

affc <- text %>%
  count(id,gender,word, sort = TRUE) %>%
  inner_join(affword, "word")

Calculating the mean of the scores for each abtract (weighted by number of time the word appears) by gender:

affc %>% group_by(id, gender) %>%
  summarise(mean.score = mean(value),
            weig.score = weighted.mean(value,n)) %>%
  ggplot(aes(x=gender,y=weig.score)) +
  geom_violin() +
  geom_boxplot(width=0.1) +
    geom_quasirandom()+
  ggtitle("Mean words score per abstract and gender")

Professors

affword <- get_sentiments("afinn")

affc <- textP %>%
  count(id,gender,word, sort = TRUE) %>%
  inner_join(affword, "word")

Calculating the mean of the scores for each abtract (weighted by number of time the word appears) by gender:

affc %>% group_by(id, gender) %>%
  summarise(mean.score = mean(value),
            weig.score = weighted.mean(value,n)) %>%
  ggplot(aes(x=gender,y=weig.score)) +
  geom_violin() +
  geom_boxplot(width=0.1) +
    geom_quasirandom()+
  ggtitle("Mean words score per abstract and gender")

Frequency of sentiment words per abstract

As classificações das palavras não me parecem muito acuradas com a linguagem científica.

Precisa saber como ponderar pelo total de palavras.

All data

nrcword <- get_sentiments("nrc")

nrc <- text %>%
  count(id,gender,word, sort = TRUE) %>%
  inner_join(nrcword, "word") %>%
  group_by(id,gender,sentiment) %>%
  summarise(n= sum(n))

ggplot(nrc, aes(x=gender, y=n)) +
  facet_wrap(~sentiment) +
  geom_violin() +
    geom_quasirandom()

Professors

nrcword <- get_sentiments("nrc")

nrc <- textP %>%
  count(id,gender,word, sort = TRUE) %>%
  inner_join(nrcword, "word") %>%
  group_by(id,gender,sentiment) %>%
  summarise(n= sum(n))

ggplot(nrc, aes(x=gender, y=n)) +
  facet_wrap(~sentiment) +
  geom_violin()+
    geom_quasirandom()

nrc %>% filter(sentiment == "positive") %>%
ggplot( aes(x=gender, y=n)) +
  geom_violin() +
  geom_boxplot(width=0.2) +
    geom_quasirandom()+
  ggtitle("Positive words")

Frequency of sentiment words per abstract

All data

bingword <- get_sentiments("bing")

bing <- text %>%
  count(id,gender,word, sort = TRUE) %>%
  inner_join(bingword, "word") %>%
  group_by(id,gender,sentiment) %>%
  summarise(n= sum(n))

ggplot(bing, aes(x=sentiment, y=n)) +
  facet_wrap(~gender) +
  geom_violin()+
    geom_quasirandom()

Professors

bingword <- get_sentiments("bing")

bing <- textP %>%
  count(id,gender,word, sort = TRUE) %>%
  inner_join(bingword, "word") %>%
  group_by(id,gender,sentiment) %>%
  summarise(n= sum(n))

ggplot(bing, aes(x=sentiment, y=n)) +
  facet_wrap(~gender) +
  geom_violin() +
  geom_boxplot(width=0.2) +
    geom_quasirandom()